Each pixel in an image is too weak to be a discriminant for any image
content. Thus, we group a set of neighboring pixels to form a basic
unit. Typical units are squared blocks of size \(3\times 3\)  or \(5 \times 5\) . There exists correlation among pixels in PixelHop units. The
correlation can be reduced or removed by signal transforms. Transform
kernels can be either predefined or derived from the input data. The
discrete cosine transform (DCT) and the wavelet transform use predefined
kernels while the Karhunen-Lo\(\acute{e}\) ve transform (KLT) adopts the
data-driven transform kernel. Two new data-driven transforms have been
introduced to tackle Subproblem 1 in the GL setting. They are the Saab
(Subspace approximation via adjusted bias) transform
[1]} and the Saak (Subspace approximation via
augmented kernels) transform [2]}. These new transforms
will be reviewed in Sec. REF . No supervision is
needed in learning the approximating subspace.
